Ligand and Structure-Based Models for the Prediction of Ligand-Receptor Affinities and Virtual Screenings: Development and Application to the β2-Adrenergic Receptor
Ligand and Structure-Based Models for the Prediction of Ligand-Receptor Affinities and Virtual Screenings: Development and Application to the β2-Adrenergic Receptor
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DOI：
10.1002/jcc.21346
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发表时间：
2010-03-01
影响因子：
3
通讯作者：
Costanzi, Stefano
中科院分区：
文献类型：
--
作者：
Vilar, Santiago;Karpiak, Joel;Costanzi, Stefano
在这项研究中，我们评估了基于配体和基于结构的模型对β（2）-肾上腺素能受体（G蛋白偶联受体（GPCR））配体的定量亲和力预测和虚拟筛选的适用性。我们还设计和评估了一些共识模型，通过偏最小二乘回归，结合联合收割机的各个组件的优势。在所有情况下，每个配体的生物活性构象来自受体晶体结构处的分子对接。我们确定了适用于不同场景的最有效的模型，无论是否存在训练集。当训练集可用时，为了对密切相关的类似物的亲和力进行排名，基于配体的一致性模型（LI-CM）似乎是最佳选择，而基于结构的MM-GBSA评分似乎是在缺乏训练集的情况下的最佳选择。对于虚拟筛选目的，发现基于结构的MM-GBSA评分是首选方法。共识模型始终具有优于或接近最佳单个组件的上级性能，并且具有显着增强的稳健性。给定多个模型，它们的预测能力没有先验知识，构建一个共识模型可以确保结果非常接近最好的模型单独产生的结果。(C)2009 Wiley Periodicals，Inc.* J Comput Chem 31：707-720，2010
In this study, we evaluated the applicability of ligand-based and structure-based models to quantitative affinity predictions and virtual screenings for ligands of the beta(2)-adrenergic receptor, a G protein-coupled receptor (GPCR). We also devised and evaluated a number of consensus models obtained through partial least square regressions, to combine the strengths of the individual components. In all cases, the bioactive conformation of each ligand was derived from molecular docking at the crystal structure of the receptor. We identified the most effective models applicable to the different scenarios, in the presence or in the absence of a training set. For ranking the affinity of closely related analogs when a training set is available, a ligand-based consensus model (LI-CM) seems to be the best choice, while the structure-based MM-GBSA score seems the best alternative in the absence of a training set. For virtual screening purposes, the structure-based MM-GBSA score was found to be the method of choice. Consensus models consistently had performances superior or close to those of the best individual components, and were endowed with a significantly increased robustness. Given multiple models with no a priori knowledge of their predictive capabilities, constructing a consensus model ensures results very close to those that the best model alone would have yielded. (C) 2009 Wiley Periodicals, Inc.* J Comput Chem 31: 707-720, 2010